With the development of network technologies, services such as video-on-demand, network television, and viewphone have become main services of a broadband network, and these services will become main services of a 3rd generation (3G) wireless network. In order to reduce resources occupied during a storage or transmission process of a video signal, compression processing is performed on the video signal at a sending end and then the video signal is transmitted to a receiving end, and the receiving end recovers the video signal through decompression processing and then plays the video signal.
Network video quality is influenced by many complex factors, such as service quality of a transmission channel, an adaptation condition between a parameter at a video encoding/decoding end and the transmission channel, where different positions loss in the video data loss occurred due to a channel packet loss, a delay, or the like, impact the video subjective quality greatly; and a video rebuffering or jitter caused by a channel delay and jitter. In order to ensure service quality of a network video, quality of a network video service needs to be monitored, so as to take corresponding measures in time to perform adjustment and maintenance, and ensure a normal operation of the video service.
A network video quality assessment is an indispensable and important technology in a network video application. However, a subjective quality assessment performed by observing with human eyes consumes time and energy, and is obviously infeasible for the network video application. Even most general and objective video quality assessment methods are not applicable to an application environment of the network video. According to degrees of demands on an original reference video, an objective video quality assessment method in the prior art is generally divided into three kinds: full reference, reduced reference, and no reference objective video quality assessments.
In an actual product application, algorithmic complexity is a factor that needs to be considered. Real-time monitoring and assessment need to be supported and performed on a terminal device (a network device or a test device) with a low computational capability. Due to the limitation of a channel bandwidth, a video receiving end usually cannot obtain a required reference video sequence, and generally, a video stream transmitted in the network needs to be assessed by using the no reference video quality assessment method.
A no reference video quality assessment model may assess quality of the video stream by using network information, a network data packet, and information of the video stream itself. If the video stream can be parsed completely, the assessment is relatively accurate. However, because the parsing of the video stream is of high complexity, the video quality assessment cannot be performed in real time or is not applicable to the terminal device (the network device or the test device) with a low computational capability.
In a video quality assessment method, influences of encoding quality Qualencoding, a video rebuffering Qualbuff, and a network packet loss Qualpl are considered in video quality TOT_MOSpred:TOT_MOSpred=func(Qualencoding,Qualbuff,Qualpl).
Influences of a bit stream x and an encoding type are considered in the encoding quality:Qualencoding=c0−c1·e−λ·x,
where c0, c1, and λ are constants, and may have different values under different encoding formats.
The network packet loss quality is calculated by using a packet loss rate, and an average packet loss rate in a sliding window is calculated first:
      PLR    mean    =            1      N        ·                  ∑                  i          =          1                N            ⁢                        PLR          i                .            
Maximum packet loss rates PLRu and PLRl are preset; if the packet loss rate in the sliding window is greater than PLRu, it is considered that the video quality is the worst, and if the packet loss rate is less than PLRl, it is considered that the packet loss at this time has no influence on the video quality:PLRi=min(PLRj,PLRu), and PLRi=max(PLRj,PLRl).
Quality of a packet loss in a period of time is:
            Qual      pl        =                  const        ·                  (                                    Qual              encoding                        -            1                    )                ·        ξ            +      1                  ξ      =                                    PLR            u                    -                      PLR            mean                                                PLR            u                    -                      PLR            l                                ,          0      ≤      ξ      ≤      1.      
Influences of the number of video rebufferings in a period of time, a re-buffering duration, and an initial buffering duration are considered in the influence quality of the video rebuffering, and a model is as follows:Qualbuff=C0+C1·INIT_PERC+C2·BUF_PERC+C3·BUF_FRQ.Final video quality is:TOT_MOSpred=Qualpl−Qualbuff.
In the foregoing video quality assessment method, a packet loss rate is calculated by using packet header information of a real-time transport protocol (RTP) packet, and video quality is calculated by using the packet loss rate, thereby reducing complexity; because only the packet loss rate is considered in the method, the calculation is simple but accuracy is low.